Assessment and Establishment of Correlation between Reactive Oxidation Species, Citric Acid, and Fructose Level in Infertile Male Individuals: A Machine-Learning Approach

Exploiting CO2 laser to boost graphite inks electron transfer for fructose biosensing in biological fluids

The possibility to print electronics by means of office tools has remarkedly increased the possibility to design affordable and robust point-of-care/need devices. However, conductive inks suffer from low electrochemical and rheological performances limiting their applicability in biosensors. Herein, a fast CO2 laser approach to activate printed carbon inks towards direct enzymatic bioelectrocatalysis (3rd generation) is proposed and exploited to build biosensors for D-fructose analysis in biological fluids. The CO2 laser treatment was compared with two lab-grade printed transducers fabricated with solvent (SB) and water (WB) based carbon inks. The use of the laser revealed significant morpho-chemical variations on the printed inks and was investigated towards enzymatic direct catalysis, using Fructose dehydrogenase (FDH) integrated into entirely lab-produced biosensors. The laser-driven activation of the inks unveils the inks' direct electron transfer (DET) ability between FDH and the electrode surface. Sub-micromolar limits of detection (SB-ink LOD = 0.47 μM; WB-ink LOD = 0.24 μM) and good linear ranges (SB-ink: 5-100 μM; WB-ink: 1-50 μM) were obtained, together with high selectivity due to use of the enzyme and the low applied overpotential (0.15 V vs. pseudo-Ag/AgCl). The laser-activated biosensors were successfully used for D-fructose determination in complex synthetic and real biological fluids (recoveries: 93-112%; RSD ≤8.0%, n = 3); in addition, the biosensor ability for continuous measurement (1.5h) was also demonstrated simulating physiological D-fructose fluctuations in cerebrospinal fluid.

Exploring correlations: Human seminal plasma and blood serum biochemistry in relation to semen quality

Male infertility is a pressing global issue, prompting the need for biomarkers correlating with seminal parameters for diagnosis. Our study investigated 10 biochemical and energetic parameters in the seminal plasma and blood sera of fertile (25 subjects) and infertile (88 subjects) Polish men, correlations between their levels in seminal plasma and semen quality, and correlations between blood sera and seminal plasma levels of examined parameters. Infertile men displayed elevated seminal plasma glucose and fructose but reduced HDL levels compared to fertile men. We observed also weak negative correlations between seminal plasma triglycerides and sperm concentration in both groups. Moreover, infertile men exhibited positive correlations between seminal plasma HDL/LDL concentrations and sperm concentration. Fertile men showed moderate negative correlations between glucose/triglycerides concentrations and sperm count and between seminal plasma triglycerides levels and sperm vitality. Semen volume correlated with triglycerides (negative) and fructose (positive) concentrations in infertile men. Sperm motility correlated negatively with total cholesterol, LDL, and triglycerides concentrations in fertile men, and weakly with AMP-activated protein kinase in infertile men. Weak negative correlations between seminal plasma fructose/AMP-activated protein kinase concentrations and sperm progressive motility were observed in infertile men, whereas in fertile men seminal plasma AMP-activated protein kinase levels were positively correlated with progressive motility. Correlation analysis between blood serum and seminal plasma parameters revealed intriguing connections, notably regarding LDL, AMP-activated protein kinase, and carnitine, suggesting systemic influences on seminal plasma composition. These findings emphasize the complex interplay between metabolic factors and sperm parameters, offering promising directions for future research in male infertility diagnostics and therapeutics.

Oocyte Quality and Blastocyst Formation Rate with Dual Stimulation in Patients Belonging to POSEIDON Groups 3 and 4: A Retrospective Comparative Study

Aim

To evaluate the oocyte retrieval rate and blastocyst formation rate with DuoStim protocol in patients belonging to POSEIDON groups 3 and 4.

Methods

This observational, retrospective, single-center study including 90 patients belonging to POSEIDON groups 3 and 4 was conducted at a tertiary care hospital from October 2017 to March 2020. Patients were allocated into two groups based on POSEIDON classification criteria: group A (POSEIDON group 3) and group B (POSEIDON group 4). DuoStim protocol was performed with human menopausal gonadotropin (hMG) at 225 IU and 300 IU in groups A and B, respectively. Study groups were again subdivided by considering the phase in which stimulation had been done [follicular phase stimulation (FPS) and luteal phase stimulation (LPS)], and then, inference was made accordingly in terms of oocytes retrieval rate and blastocysts formation rate. Data were compiled and analyzed using statistical software SPSS version 20.

Results

The baseline characteristics of two groups were compatible with POSEIDON groups 3 and 4. A significant difference was found between study groups with respect to age and anti-mullerian hormone levels (p < 0.05). Significantly, a greater number of oocytes and blastocysts were obtained in LPS stage, substantially more in group A (3.69 ± 3.4 vs. 4.52 ± 4.3 and 1.36 ± 0.65 vs. 3.17 ± 1.84) than group B (2.2 ± 1.36 vs. 3.6 ± 4.5 and 0.41 ± 0.8 vs. 1.29 ± 2.04). A greater blastulation rate (50 vs. 66.7% and 33.3 vs. 50%) and 100% oocyte maturity rate were observed in LPS stage of both the study groups.

Conclusion

In patients belonging to POSEIDON groups 3 and 4, the number of oocytes retrieved and blastocyst formation rate were greater in LPS stage when compared to FPS with DuoStim protocol.

Emerging Hallmarks of Metabolic Reprogramming in Prostate Cancer

Prostate cancer (PCa) is the most common male malignancy and the fifth leading cause of cancer death in men worldwide. Prostate cancer cells are characterized by a hybrid glycolytic/oxidative phosphorylation phenotype determined by androgen receptor signaling. An increased lipogenesis and cholesterogenesis have been described in PCa cells. Many studies have shown that enzymes involved in these pathways are overexpressed in PCa. Glutamine becomes an essential amino acid for PCa cells, and its metabolism is thought to become an attractive therapeutic target. A crosstalk between cancer and stromal cells occurs in the tumor microenvironment because of the release of different cytokines and growth factors and due to changes in the extracellular matrix. A deeper insight into the metabolic changes may be obtained by a multi-omic approach integrating genomics, transcriptomics, metabolomics, lipidomics, and radiomics data.

Seminal fructose and citric acid concentrations relative to sperm parameters among men for fertility evaluation in Yaoundé, Cameroon

Aim: Given the male infertility’s pluri-etiological nature, thorough examinations are needed for its evaluation. Fructose and citric acid are simple biomolecules, easy to assay, which provide reliable information on the seminal vesicles and prostate, respectively. This study aimed to compare the seminal fructose and citric acid levels in men undergoing fertility evaluation and determine the relation between these markers and sperm parameters. Methods: A prospective cross-sectional study was conducted on consenting male participants. Following 2010 seminal fluid analysis (SFA) manual of World Health Organization (WHO), semen samples were analyzed for several sperm parameters, seminal fructose and citric acid. Statistical analyses were performed using IBM SPSS 24.0 software. Significant statistical difference was considered at P < 0.05. Results: There is no significant difference between seminal fructose and citric acid levels amongst men with normal and abnormal sperm parameters as median seminal fructose and citric acid levels were 11.1 (7.4–17.1) mg/mL and 11.4 (7.3–15.2) mg/mL respectively (P ≥ 0.05). However, a high level of fructose was observed in the two groups according to the reference value. The study revealed a significant positive correlation between seminal fructose levels and semen volume (coefficient rho = 0.663; P = 0.001) and between seminal citric acid levels and semen volume (coefficient rho = 0.319; P = 0.004). Conclusions: These biomarkers secretions can serve as markers of the state of their respective secreting glands and hence play a vital role in the investigation of male infertility.

Multiple metabolic pathways fuel the truncated tricarboxylic acid cycle of the prostate to sustain constant citrate production and secretion

Objective

The prostate is metabolically unique: it produces high levels of citrate for secretion via a truncated tricarboxylic acid (TCA) cycle to maintain male fertility. In prostate cancer (PCa), this phenotype is reprogrammed, making it an interesting therapeutic target. However, how the truncated prostate TCA cycle works is still not completely understood.

Methods

We optimized targeted metabolomics in mouse and human organoid models in ex vivo primary culture. We then used stable isotope tracer analyses to identify the pathways that fuel citrate synthesis.

Results

First, mouse and human organoids were shown to recapitulate the unique citrate-secretory program of the prostate, thus representing a novel model that reproduces this unusual metabolic profile. Using stable isotope tracer analysis, several key nutrients were shown to allow the completion of the prostate TCA cycle, revealing a much more complex metabolic profile than originally anticipated. Indeed, along with the known pathway of aspartate replenishing oxaloacetate, glutamine was shown to fuel citrate synthesis through both glutaminolysis and reductive carboxylation in a GLS1-dependent manner. In human organoids, aspartate entered the TCA cycle at the malate entry point, upstream of oxaloacetate. Our results demonstrate that the citrate-secretory phenotype of prostate organoids is supported by the known aspartate–oxaloacetate–citrate pathway, but also by at least three additional pathways: glutaminolysis, reductive carboxylation, and aspartate–malate conversion.

Conclusions

Our results add a significant new dimension to the prostate citrate-secretory phenotype, with at least four distinct pathways being involved in citrate synthesis. Better understanding this distinctive citrate metabolic program will have applications in both male fertility as well as in the development of novel targeted anti-metabolic therapies for PCa.

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Targeted metabolomics and stable isotope tracer analysis were optimized in mouse and human prostate organoids.

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Organoids recapitulate the unique citrate-secretory phenotype of the prostate.

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Glutamine fuels citrate synthesis for secretion by glutaminolysis and reductive carboxylation.

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Aspartate enters the TCA cycle at different entry points in mouse and human prostate organoids for citrate production.

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We revealed a much more complex TCA cycle in the prostate than originally anticipated.

Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art

Artificial intelligence (AI) and machine learning models are today frequently used for classification and prediction of various biochemical processes and phenomena. In recent years, numerous research efforts have been focused on developing such models for assessment, categorization, and prediction of oxidative stress. Supervised machine learning can successfully automate the process of evaluation and quantification of oxidative damage in biological samples, as well as extract useful data from the abundance of experimental results. In this concise review, we cover the possible applications of neural networks, decision trees and regression analysis as three common strategies in machine learning. We also review recent works on the various weaknesses and limitations of artificial intelligence in biochemistry and related scientific areas. Finally, we discuss future innovative approaches on the ways how AI can contribute to the automation of oxidative stress measurement and diagnosis of diseases associated with oxidative damage.